Bibliographic record
Abstract
Malevolent Eldritch Shrieking is a large diverse international salon-style multigenerational exhibition about painting (in some aspect) including Jan Albers, Sue Arrowsmith, Paul Barlow, Eric Bainbridge, Anthony Bennett, Michael Bevilacqua, Merijn Bolink, Juan Bolivar, Kate Bright, Ralf Broeg, Glenn Brown, Simon Campbell, John Chilver, Sacha Craddock, Michael Craig-Martin, Hansjoerg Dobliar, Rowena Dring, Marcel van Eeden, Machiko Edmondson, Tim Eitel, Roberto Ekholm, Dee Ferris, Gail Fitzgerald, Saul Fletcher, Ed Fornieles, Torben Giehler, Lothar Goetz, Deme Georghiou, Brian Griffiths, Terry Haggerty, Jane Harris, Matthew Harrison, Karolyn Hatton, Gerard Hemsworth, Gregor Hildebrandt, Stefan Hirsig, Dale Holmes, Paul Housley, Tom Howse, Des Hughes, Richard Jacobs, Ben Judd, Ben Kaufmann, Scott King, Richard Kirwan, Rannva Kunoy, Des Lawrence, Christoph Lohmann, Bob Matthews, Caroline McCarthy, Penny McCarthy, Peter McDonald, Dominic McGill, Robert McNally, Dawn Mellor, Nathaniel Mellors, Jo Melvin, Robert Moon, Ryan Mosley, Jost Münster, Julian Opie, Carl Ostendarp, Helena Petersen, Michael Petry, Daniel Pettitt, Jan van der Ploeg, James Pyman, Ged Quinn, Barry Reigate, Bernd Ribbeck, Darren Richardson, Mark Riddington, David Risley, Ben Rivers, Neil Rumming, Lesley Sanderson & Neil Conroy, Sophie Schmidt, Gary Simmonds, Dillwyn Smith, Maxima Smith, Stephen Snoddy, Michael Stubbs, Daniel Sturgis, Srinivas Surti, Tomoaki Suzuki, Finlay Taylor, David Thorpe, Dimitra Vamiali, Riette Wanders, Mathew Weir, Richard Wentworth, Lucy Williams, Keith Wilson, Martin Wöhrl, Clare Woods, Richard Woods, Will Yackulic and more.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".